Solving Dynamic Economic Emission Dispatch Based on Period Coupling Characteristic

doi:10.3969/j.issn.1000－7229.2016.10.020

Abstract

Abstract: This paper presents a new improved teaching-learning-based optimization algorithm (ITLBO) to solve the dynamic economic emission dispatch (DEED) problem based on the characteristics of period coupling. DEED is a bi-objective optimization problem, which minimizes the fuel cost and emission level simultaneously. In the proposed algorithm, the opposition-based learning (OBL) strategy is employed to improve the population diversity, the single interval teaching and learning process is used to enhance the local searching ability, and the single interval greedy selection strategy is adopted to explore a new domain in the whole searching space, aiming at balancing the local optimization and global optimization ability. Through the simulation analysis on the ten-unit 39-nodes system, the results show that the proposed strategy has a faster convergence rate and better convergence characteristic, and can obtain higher quality solutions.

Key words: dynamic economic emission dispatch（DEED), teaching-learning-based optimization algorithm, greedy selection, period coupling characteristic

CLC Number:

TM 731

WANG Xiaofei, HU Zhijian, ZHANG Menglin,HU Meiyu, WANG Xiang, DENG Aopan. Solving Dynamic Economic Emission Dispatch Based on Period Coupling Characteristic[J]. Electric Power Construction, 2016, 37(10): 144-.

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